96 citations · 334 across the 43 of their papers we have counts for
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DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee +4
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its…
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the v…
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Wenjia Bai, Matthew Sinclair, Giacomo Tarroni +20
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accura…
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
Benjamin Hou, Bishesh Khanal, Amir Alansary +7
Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance…
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas +10
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where image…
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Benjamin Hou, Amir Alansary, Steven McDonagh +6
This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the…